Vinuthna S

Developed end-to-end ML pipelines on Azure ML, improving deployment efficiency by 30% across scalable healthcare analytics workflows. Implemented NLP transformer models to extract structured clinical insights from unstructured medical records, directly accelerating enterprise healthcare analytics. Built PySpark feature engineering pipelines for large-scale healthcare datasets, cutting preprocessing latency and feeding optimized inputs into downstream ML models. Developed anomaly detection systems using unsupervised learning for early risk identification in clinical operations, enabling proactive decision-making at scale. Built treatment recommendation engines using collaborative filtering, improving clinical recommendation accuracy by 21% for healthcare professionals. Automated model monitoring and drift detection using MLflow, ensuring governance and reliability across all production ML environments. Enabled CI/CD pipelines via Azure DevOps and validated data quality with Great Expectations, reducing deployment friction and ensuring data integrity compliance.

Developed fraud detection models using Scikit-learn, improving transaction risk accuracy by 26% across real-time financial systems. Deployed scalable ML solutions on AWS SageMaker with automated training, versioning, and deployment pipelines for enterprise production environments. Built deep learning models with PyTorch and dimensionality reduction, improving classification accuracy by 25% on large-scale financial datasets. Designed NLP sentiment analysis pipelines using transformer techniques for customer feedback classification, driving measurable digital banking experience improvements. Integrated SHAP explainability, REST APIs, A/B testing, and AWS CloudWatch monitoring, ensuring regulatory-grade transparency and production reliability across all ML initiatives. Automated ETL pipelines using Apache Airflow and AWS services, improving data availability and reducing workflow failures across analytics teams.

Built churn prediction models using Python and Scikit-learn, improving forecast accuracy by 24% across telecom customer datasets. Designed customer segmentation models (classification and clustering) powering targeted marketing campaigns with measurable engagement lift. Developed revenue forecasting models using regression, delivering predictive insights that directly supported financial planning decisions. Automated reporting workflows via Python ETL scripting, reducing manual effort by 30% and improving consistency of analytics deliverables. Engineered scalable SQL and Spark data pipelines to integrate and transform multi-source enterprise datasets supporting advanced analytics and reporting.